Study on Ecological Change Remote Sensing Monitoring Method Based on Elman Dynamic Recurrent Neural Network

In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.

Long time data series and data stewardship reference model

The need for accessing historical Earth Observation(EO)data series strongly increased in the last ten years,particularly for long-term science and environmental monitoring applications.This trend is likely to increase even more in the future,in particular regarding the growing interest on global change monitoring which is driving users to request time-series of data spanning 20 years and more,and also due to the need to support the United Nations Framework Convention on Climate Change(UNFCCC).While much of the satellite observations are accessible from different data centers,the solution for analyzing measurements collected from various instruments for time series analysis is both difficult and critical.Climate research is a big data problem that involves high data volume of measurements,methods for on-the-fly extraction and reduction to keep up with the speed and data volume,and the ability to address uncertainties from data collections,processing,and analysis.The content of EO data archives is extending from a few years to decades and therefore,their value as a scientific time-series is continuously increasing.Hence there is a strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable.The preservation of EO space data can also be considered as responsibility of the Space Agencies or data owners as they constitute a humankind asset.This publication aims at describing the activities supported by the European Space Agency relating to the Long Time Series generation with all relevant best practices and models needed to organise and measure the preservation and stewardship processes.The Data Stewardship Reference Model has been defined to give an overview and a way to help the data owners and space agencies in order to preserve and curate the space datasets to be ready for long time data series composition and analysis.

Asymptotic Properties of Wavelet Estimators in Partially Linear Errors-in-variables Models with Long-memory Errors

While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables（EV） model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.